{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "937012ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date  close   open    high    low  pre_close  change  \\\n",
      "0     920128.SZ   20250331  33.44  34.95   41.59  32.02      34.34   -0.90   \n",
      "1     920118.SZ   20250331  28.16  29.00   33.97  27.66      28.72   -0.56   \n",
      "2     920116.SZ   20250331  88.80  66.80  139.99  63.20      65.45   23.35   \n",
      "3     920111.SZ   20250331  29.35  29.20   37.86  27.86      29.06    0.29   \n",
      "4     920108.SZ   20250331  22.80  21.70   33.85  20.36      22.46    0.34   \n",
      "...         ...        ...    ...    ...     ...    ...        ...     ...   \n",
      "5995  688448.SH   20250228  31.50  29.10   34.60  29.00      28.90    2.60   \n",
      "5996  688443.SH   20250228  24.92  25.00   26.75  23.15      24.74    0.18   \n",
      "5997  688439.SH   20250228  56.10  47.80   58.18  47.30      47.33    8.77   \n",
      "5998  688435.SH   20250228  37.14  29.87   55.18  29.87      29.08    8.06   \n",
      "5999  688433.SH   20250228  40.65  25.40   50.80  24.55      25.40   15.25   \n",
      "\n",
      "      pct_chg          vol        amount  \n",
      "0     -0.0262   73362410.0  2.748314e+09  \n",
      "1     -0.0195   33496871.0  1.044348e+09  \n",
      "2      0.3568  132611254.0  1.340631e+10  \n",
      "3      0.0100  126242643.0  4.087065e+09  \n",
      "4      0.0151  151892963.0  3.913377e+09  \n",
      "...       ...          ...           ...  \n",
      "5995   0.0900   32793764.0  1.049612e+09  \n",
      "5996   0.0073   62740577.0  1.559739e+09  \n",
      "5997   0.1853   61428869.0  3.218010e+09  \n",
      "5998   0.2772  112197729.0  4.819989e+09  \n",
      "5999   0.6004   91073705.0  3.280140e+09  \n",
      "\n",
      "[6000 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 导入tushare 股票月线行情\n",
    "import tushare as ts\n",
    "# 初始化pro接口\n",
    "pro = ts.pro_api('1c7f85b9026518588c0d0cdac712c2d17344332c9c8cfe6bc83ee75c')\n",
    "\n",
    "# 拉取数据\n",
    "df = pro.monthly(**{\n",
    "    \"ts_code\": \"\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": \"\",\n",
    "    \"end_date\": \"\",\n",
    "    \"limit\": \"\",\n",
    "    \"offset\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"close\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51604f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "from datetime import datetime\n",
    "\n",
    "# 设置绘图风格和中文字体\n",
    "plt.style.use('seaborn')\n",
    "sns.set_palette(\"husl\")\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "class StockTradingStrategy:\n",
    "    def __init__(self, df):\n",
    "        \"\"\"\n",
    "        初始化交易策略\n",
    "        \n",
    "        参数:\n",
    "        df: 包含多只股票数据的DataFrame (新格式)\n",
    "        \"\"\"\n",
    "        self.df = df.copy()\n",
    "        self.results = {}\n",
    "        \n",
    "    def preprocess_data(self):\n",
    "        \"\"\"\n",
    "        数据预处理:\n",
    "        1. 转换日期格式\n",
    "        2. 按股票代码和日期排序\n",
    "        3. 计算技术指标\n",
    "        \"\"\"\n",
    "        # 转换日期格式\n",
    "        self.df['trade_date'] = pd.to_datetime(self.df['trade_date'], format='%Y%m%d')\n",
    "        \n",
    "        # 按股票代码和日期排序\n",
    "        self.df = self.df.sort_values(['ts_code', 'trade_date'])\n",
    "        \n",
    "        # 计算技术指标\n",
    "        def calculate_technical(group):\n",
    "            # 计算均线\n",
    "            group['ma5'] = group['close'].rolling(5).mean()\n",
    "            group['ma20'] = group['close'].rolling(20).mean()\n",
    "            \n",
    "            # 计算MACD\n",
    "            exp12 = group['close'].ewm(span=12, adjust=False).mean()\n",
    "            exp26 = group['close'].ewm(span=26, adjust=False).mean()\n",
    "            group['macd'] = exp12 - exp26\n",
    "            group['signal'] = group['macd'].ewm(span=9, adjust=False).mean()\n",
    "            group['hist'] = group['macd'] - group['signal']\n",
    "            \n",
    "            # 计算RSI\n",
    "            delta = group['close'].diff()\n",
    "            gain = delta.where(delta > 0, 0)\n",
    "            loss = -delta.where(delta < 0, 0)\n",
    "            avg_gain = gain.rolling(14).mean()\n",
    "            avg_loss = loss.rolling(14).mean()\n",
    "            rs = avg_gain / avg_loss\n",
    "            group['rsi'] = 100 - (100 / (1 + rs))\n",
    "            \n",
    "            # 计算布林带\n",
    "            group['upper_band'] = group['close'].rolling(20).mean() + 2 * group['close'].rolling(20).std()\n",
    "            group['lower_band'] = group['close'].rolling(20).mean() - 2 * group['close'].rolling(20).std()\n",
    "            \n",
    "            # 计算量比 (基于过去5日平均成交量)\n",
    "            group['vol_ratio'] = group['vol'] / group['vol'].rolling(5).mean()\n",
    "            \n",
    "            # 计算涨跌幅\n",
    "            group['pct_chg'] = group['close'].pct_change() * 100\n",
    "            \n",
    "            return group\n",
    "        \n",
    "        self.df = self.df.groupby('ts_code').apply(calculate_technical)\n",
    "        \n",
    "    def generate_signals(self):\n",
    "        \"\"\"\n",
    "        生成交易信号:\n",
    "        1. 买入信号: 5日均线上穿20日均线且量比>1\n",
    "        2. 卖出信号: 5日均线下穿20日均线或达到止损止盈条件\n",
    "        \"\"\"\n",
    "        def generate_for_stock(group):\n",
    "            # 初始化信号列\n",
    "            group['signal'] = 0  # 0表示无信号，1表示买入，-1表示卖出\n",
    "            \n",
    "            # 生成买入信号 (5日均线上穿20日均线且量比>1)\n",
    "            buy_condition = (group['ma5'] > group['ma20']) & (group['ma5'].shift(1) <= group['ma20'].shift(1)) & (group['vol_ratio'] > 1)\n",
    "            group.loc[buy_condition, 'signal'] = 1\n",
    "            \n",
    "            # 生成卖出信号 (5日均线下穿20日均线)\n",
    "            sell_condition = (group['ma5'] < group['ma20']) & (group['ma5'].shift(1) >= group['ma20'].shift(1))\n",
    "            group.loc[sell_condition, 'signal'] = -1\n",
    "            \n",
    "            return group\n",
    "        \n",
    "        self.df = self.df.groupby('ts_code').apply(generate_for_stock)\n",
    "    \n",
    "    def backtest_strategy(self, initial_capital=100000, stop_loss=0.95, take_profit=1.20):\n",
    "        \"\"\"\n",
    "        回测策略\n",
    "        \n",
    "        参数:\n",
    "        initial_capital: 初始资金\n",
    "        stop_loss: 止损比例 (例如0.95表示5%止损)\n",
    "        take_profit: 止盈比例 (例如1.20表示20%止盈)\n",
    "        \"\"\"\n",
    "        # 按股票分组回测\n",
    "        grouped = self.df.groupby('ts_code')\n",
    "        \n",
    "        for name, group in tqdm(grouped, desc=\"回测进度\"):\n",
    "            capital = initial_capital\n",
    "            position = 0\n",
    "            entry_price = 0\n",
    "            returns = []\n",
    "            equity = []\n",
    "            signals = []\n",
    "            \n",
    "            for i, row in group.iterrows():\n",
    "                # 如果有持仓，检查止损止盈\n",
    "                if position > 0:\n",
    "                    current_return = row['close'] / entry_price\n",
    "                    \n",
    "                    # 检查止损\n",
    "                    if current_return < stop_loss:\n",
    "                        capital = position * row['close']\n",
    "                        position = 0\n",
    "                        signals.append(-1)  # 止损卖出信号\n",
    "                    \n",
    "                    # 检查止盈\n",
    "                    elif current_return > take_profit:\n",
    "                        capital = position * row['close']\n",
    "                        position = 0\n",
    "                        signals.append(-1)  # 止盈卖出信号\n",
    "                    else:\n",
    "                        signals.append(0)\n",
    "                else:\n",
    "                    signals.append(0)\n",
    "                \n",
    "                # 处理买入信号\n",
    "                if row['signal'] == 1 and position == 0:\n",
    "                    position = capital / row['close']\n",
    "                    entry_price = row['close']\n",
    "                    capital = 0\n",
    "                    signals[-1] = 1  # 覆盖当前信号为买入\n",
    "                \n",
    "                # 处理卖出信号\n",
    "                elif row['signal'] == -1 and position > 0:\n",
    "                    capital = position * row['close']\n",
    "                    position = 0\n",
    "                    signals[-1] = -1  # 覆盖当前信号为卖出\n",
    "                \n",
    "                # 计算当前权益\n",
    "                current_equity = capital + position * row['close'] if position > 0 else capital\n",
    "                equity.append(current_equity)\n",
    "                returns.append((current_equity / initial_capital) - 1)\n",
    "            \n",
    "            # 保存结果\n",
    "            result = group.copy()\n",
    "            result['equity'] = equity\n",
    "            result['returns'] = returns\n",
    "            result['trade_signal'] = signals\n",
    "            self.results[name] = result\n",
    "    \n",
    "    def select_and_rank_stocks(self, date):\n",
    "        \"\"\"\n",
    "        选股与排序\n",
    "        \n",
    "        参数:\n",
    "        date: 选股日期 (str或datetime)\n",
    "        \n",
    "        返回:\n",
    "        按选股条件排序的股票列表\n",
    "        \"\"\"\n",
    "        if isinstance(date, str):\n",
    "            date = pd.to_datetime(date, format='%Y%m%d')\n",
    "        \n",
    "        # 获取指定日期的数据\n",
    "        day_data = self.df[self.df['trade_date'] == date].copy()\n",
    "        \n",
    "        if day_data.empty:\n",
    "            return pd.DataFrame()\n",
    "        \n",
    "        # 选股条件 (5日均线在20日均线之上且量比>1.2且RSI在30-70之间)\n",
    "        selected = day_data[\n",
    "            (day_data['ma5'] > day_data['ma20']) & \n",
    "            (day_data['vol_ratio'] > 1.2) & \n",
    "            (day_data['rsi'] > 30) & \n",
    "            (day_data['rsi'] < 70)\n",
    "        ].copy()\n",
    "        \n",
    "        if selected.empty:\n",
    "            return pd.DataFrame()\n",
    "        \n",
    "        # 排序条件 (按涨跌幅降序排列)\n",
    "        selected = selected.sort_values('pct_chg', ascending=False)\n",
    "        \n",
    "        return selected[['ts_code', 'trade_date', 'close', 'ma5', 'ma20', 'vol_ratio', 'rsi', 'pct_chg', 'vol']]\n",
    "    \n",
    "    def visualize_results(self, stock_code):\n",
    "        \"\"\"\n",
    "        可视化回测结果\n",
    "        \n",
    "        参数:\n",
    "        stock_code: 要可视化的股票代码\n",
    "        \"\"\"\n",
    "        if stock_code not in self.results:\n",
    "            print(f\"未找到股票 {stock_code} 的回测结果\")\n",
    "            return\n",
    "        \n",
    "        data = self.results[stock_code]\n",
    "        \n",
    "        plt.figure(figsize=(16, 12))\n",
    "        \n",
    "        # 价格和均线图\n",
    "        plt.subplot(3, 1, 1)\n",
    "        plt.plot(data['trade_date'], data['close'], label='收盘价', color='black', alpha=0.8)\n",
    "        plt.plot(data['trade_date'], data['ma5'], label='5日均线', color='blue', alpha=0.6)\n",
    "        plt.plot(data['trade_date'], data['ma20'], label='20日均线', color='red', alpha=0.6)\n",
    "        \n",
    "        # 标记买卖点\n",
    "        buy_signals = data[data['trade_signal'] == 1]\n",
    "        sell_signals = data[data['trade_signal'] == -1]\n",
    "        plt.scatter(buy_signals['trade_date'], buy_signals['close'], marker='^', color='green', label='买入信号', alpha=1)\n",
    "        plt.scatter(sell_signals['trade_date'], sell_signals['close'], marker='v', color='red', label='卖出信号', alpha=1)\n",
    "        \n",
    "        plt.title(f'{stock_code} - 价格与交易信号')\n",
    "        plt.ylabel('价格')\n",
    "        plt.legend()\n",
    "        plt.grid(True, alpha=0.3)\n",
    "        \n",
    "        # 收益率曲线\n",
    "        plt.subplot(3, 1, 2)\n",
    "        plt.plot(data['trade_date'], data['equity'], label='资金曲线', color='purple')\n",
    "        plt.title('资金曲线')\n",
    "        plt.ylabel('资金')\n",
    "        plt.grid(True, alpha=0.3)\n",
    "        \n",
    "        # 技术指标\n",
    "        plt.subplot(3, 1, 3)\n",
    "        plt.plot(data['trade_date'], data['macd'], label='MACD', color='blue')\n",
    "        plt.plot(data['trade_date'], data['signal'], label='Signal', color='red')\n",
    "        plt.bar(data['trade_date'], data['hist'], label='Histogram', color='gray', alpha=0.5)\n",
    "        plt.title('MACD指标')\n",
    "        plt.ylabel('MACD')\n",
    "        plt.legend()\n",
    "        plt.grid(True, alpha=0.3)\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    def compare_strategies(self, stock_codes):\n",
    "        \"\"\"\n",
    "        比较多只股票的收益率曲线\n",
    "        \n",
    "        参数:\n",
    "        stock_codes: 要比较的股票代码列表\n",
    "        \"\"\"\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        \n",
    "        for code in stock_codes:\n",
    "            if code in self.results:\n",
    "                data = self.results[code]\n",
    "                plt.plot(data['trade_date'], data['returns'], label=f'{code}')\n",
    "        \n",
    "        plt.title('多股票收益率比较')\n",
    "        plt.ylabel('收益率')\n",
    "        plt.xlabel('日期')\n",
    "        plt.legend()\n",
    "        plt.grid(True, alpha=0.3)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    def get_performance_metrics(self):\n",
    "        \"\"\"\n",
    "        计算并返回各股票的策略表现指标\n",
    "        \"\"\"\n",
    "        metrics = []\n",
    "        \n",
    "        for code, data in self.results.items():\n",
    "            if len(data) == 0:\n",
    "                continue\n",
    "                \n",
    "            total_return = data['returns'].iloc[-1]\n",
    "            max_equity = data['equity'].max()\n",
    "            min_equity = data['equity'].min()\n",
    "            max_drawdown = (max_equity - min_equity) / max_equity\n",
    "            \n",
    "            # 计算年化收益率\n",
    "            days = (data['trade_date'].iloc[-1] - data['trade_date'].iloc[0]).days\n",
    "            annualized_return = (1 + total_return) ** (365/days) - 1 if days > 0 else 0\n",
    "            \n",
    "            # 计算胜率\n",
    "            trades = data[data['trade_signal'] != 0]\n",
    "            if len(trades) > 0:\n",
    "                win_rate = len(trades[trades['returns'] > 0]) / len(trades)\n",
    "            else:\n",
    "                win_rate = 0\n",
    "                \n",
    "            metrics.append({\n",
    "                '股票代码': code,\n",
    "                '总收益率': total_return,\n",
    "                '年化收益率': annualized_return,\n",
    "                '最大回撤': max_drawdown,\n",
    "                '胜率': win_rate,\n",
    "                '交易次数': len(trades)\n",
    "            })\n",
    "        \n",
    "        return pd.DataFrame(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07790b2e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "回测进度: 100%|██████████████████████████████████████████████████████████████████| 5487/5487 [00:02<00:00, 2405.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选股结果:\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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V3t08kYjFJ5/4Yz/Py/NKnilTXEpKGhgxwjFq1THTpMNfMNI05WwG5WwG5WwOZW0G5WyGdMhZpU7TVOo0QaVO23AceOuteMkTDlvs3BkveXr0cCgtjc/JM3q0g2Ul8YClVaXDXzDSNOVsBuVsBuVsDmVtBuVshnTIWaVO01pa6iR9Th1JbX4/jBzpMHKkwze+0YDrwj//6WPVqiDLl/sJhy2WLAmyZEkQgC5dXEpLbUKhKKGQzbhxDsFgkk9CREREREREJAWp1JFW5fPBkCEuo0bZXHFFPQDvveeL3cVTURHg73/3vgCyslxKSuzYvDzjx9tkZibzDERERERERERSg0odaXMDBrgMGBDly1+OAkf48MN4yRMOW7z6aoBXX/X+V+zQwWXixPgy6hMn2mRlJff4RURERERERNojzakjbeJkxiHu2uWLraxVUWHxxht+XNebXTkYdCkqcgiFopSX25SU2GRnt+WRy8lIh/G90jTlbAblbAblbA5lbQblbIZ0yFlz6jRNc+pIysrNdbnggigXXBAFYO9eqKy0CIcDhMMWq1f7WbUqk1/+Evx+l3Hjji2jHqWszKZr16QevoiIiIiIiEhSqNSRdqdrV5g+3Wb6dK8drauDqqr4cK01ayzWrrX49a8z8PlcRo1yYqtrlZXZ5OYacfOZiIiIiIiIGE6ljrR72dkwbZrNtGleyXPwIKxebcWWUa+psXjjDYvf/c7bftgwr+A5VvT06aOSR0RERERERNKPSh1JOVlZMGWKzZQpXslz5AisWROfk6eqyuLxxy0ef9zbftCg+HCtUMhmwAAXny+JJyAiIiIiIiLSCjRRsrSJZE7mFY3C+vX+o8O1AkQiFvv2xVucfv2c2BLq5eVRBg9WydNS6TBpmzRNOZtBOZtBOZtDWZtBOZshHXLWRMlNa+lEySp1pE20pzce24Y33vDH5uSJRCw+/tgf+3mvXk5sPp7ycpsRIxz8/hPsUGLaU87SdpSzGZSzGZSzOZS1GZSzGdIhZ5U6TdPqVyKfw7Jg7FiHsWMdvvnNBlwX3nrLT0VFfMjWX/8a5K9/DQLQvbtDaWl8Xp4xYxwsK8knISIiIiIiIvIpKnXEOD4fjBjhMGKEw9e/7pU8W7b4Ykuoh8MWzz0X5LnnvJKnc2c3VvKEQlEKCx2CwSSfhIiIiIiIiBhPpY4Yz+eDwYNdBg9u4MorGwDYts0XK3jC4QAvvuh9QSZZWS6TJsVX1xo/3qZDh+Seg4iIiIiIiJhHpY5II/r3d+nfP8rll0eBI3z0kS82VCsctli+PMDy5d7LJzPTZcKE+HCtiRNtOnVK7vGLiIiIiIhI+tNEydIm0mEyrxPZvdsreY4VPRs2+HFdbwmtQMClqMghFIpSXm5TUmLTuWVzXrV76Z6zeJSzGZSzGZSzOZS1GZSzGdIhZ02U3DRNlCySQD17upx/fpTzz48CUFsLlZVWbF6eNWv8VFdncv/94Pe7jB3rxFbXKiuL0q1bkk9AREREREREUp5KHZFWkJMD55xjc845XqNbVwerVlmxeXnWrLFYt87ioYe87UeNis/JU1Zm06uXETfMiYiIiIiISCtSqSPSBrKz4YwzbM44wyt5Dh2C1aut2DLq1dUWGzdaPPKIt/3QocdW1/LKnr59VfKIiIiIiIjIianUEUmAjh1h8mSbyZO9kqe+Htau9ceGa1VWWjzxhMUTT3jbDxjgUF5uU14epazMZuBAF58viScgIiIiIiIi7Y4mSpY2kQ6TeSVSNAqvveaPLaEeiVjU1sZbnL5943PyhEI2Q4c67aLkUc5mUM5mUM5mUM7mUNZmUM5mSIecNVFy01o6UbJKHWkT6fDGk0yOA2+84Y+trhWJWOze7Y/9vGdPJzZUKxSyGTnSwe8/wQ7biHI2g3I2g3I2g3I2h7I2g3I2QzrkrFKnaVr9SiSN+P1QUOBQUOBw7bUNuC68/bY/VvBUVFgsXhxk8eIgAN26uZSWRmNFz5gxDgG9ukVERERERNJaUn/tmz9/Pps3b2bq1KnMnTv3pLbbvXs33/nOd3jyyScTdbgiSePzwfDhDsOHO1x9tVfybN3qiw3XCoctnn8+yPPPeyVPdrZLaemxyZejFBU5BINJPgkRERERERFpVUkrdZYuXYrjOCxcuJBbbrmFrVu3MmjQoGZt161bN37wgx9w6NChxB+4SDvg80F+vkt+fpQ5c6IAvP++V/J4d/IEeOkl7wsyycpymTgxPlxrwgSbDh2Sew4iIiIiIiLyxSSt1KmqqmLGjBkATJkyhZqamkZLnca2mz59Ovfdd98J7+75tGDQahcTy5oiELCSfQjGGTwYBg92ufLKKBDlo48gHLZYudIbtrViRYAVK7yXfEaGy8SJDpMnO0yebFNS4tCp08k/p3I2g3I2g3I2g3I2h7I2g3I2QzrkbFltOwFoRkbrXaNUu94JK3Vuv/12tmzZEvu+qqqKWbNmAZCTk8P27dsbfdzBgwfJy8s7brvs7OyTfv6GhtSeWCoVpfpkXqmue3c47zyb887zvv/4Yx+VldbRIVsWlZXeals/+1mQQMClsNAhFPLm5SkttenSpXnPo5zNoJzNoJzNoJzNoazNoJzNkOo527bTpvtv7euTStc7YaXOggULjvv+7rvv5vDhw4BX3DhO4yFnZWU1azsRObEePVxmzowyc6Y3XGvfPv6l5Amwdq2fmppMfvUr8PtdCgqco3Py2JSVRenePcknICIiIiIiIsdJ2vCrgoICampqKCoqYtOmTeTn53+h7UTk5HTpAmefbXP22TZQT10dVFfHV9davdpi/XqLhx7yth81yqaszJuXp6zMJi/PTerxi4iIiIiImC5ppc5ZZ53FnDlz2LlzJ8uXL2fRokW88847LF68mHnz5p1wOxFpfdnZcPrpNqef7t1qePgwrF7t3clTUWFRXW2xcaPFY4952w8Z4jBlikNJSQPl5Tb9+qnkERERERERSSSf67pJ+02straWlStXUlxcTG5u7hfe7kR27drf0sOUFsjIsFJqHKI0rb4e1q3zx5ZQr6y0qKuLzz4+YIATW0I9FLIZNMjV5ORpQq9nMyhnMyhncyhrMyhnM6RDzlVVFW26/5KS8lbbV7Kud25u5xY9LqmlTiKp1EmsdHjjkROLRuGttwIsW3ZsKfUAe/fGW5w+feJz8oRCNsOGOSp5UpRez2ZQzmZQzuZQ1mZQzmZIh5xV6jStpaVO0oZfiUhqCwSgqMhl9Ogo11/fgOPApk3+2HCtcNjiqaeCPPVUEICePZ3j5uQZPdrB37YrG4qIiIiIiKQ1lToi0ir8fhg92mH0aIdrrmnAdeGdd44vef72tyB/+5tX8uTkuJSVxYdrjR3rENA7koiIiIiISLPpVygRaRM+Hwwb5jBsmMNXv+qVPO++64stoV5RYfHCCwFeeMF7G8rOdikpsWPz8hQVOWRkJPkkRERERERE2jGVOiKSED4fDBrkMmhQlNmzowBs336s5PG+Xn45wMsvB4BMOnZ0mTQpvoz6hAk2HTsm9xxERERERETaE5U6IpI0/fq5zJoVZdYsr+TZudNHJBJfRn3FigArVnhvUxkZLuPHx+fkKS62yc5O5tGLiIiIiIgkl0odEWk3evVyufDCKBde6JU8e/ZAJOIN1YpELFatsqis9N62LMulsDC+jHppqU1OTjKPXkREREREJLFU6ohIu9WtG8yYEWXGDK/k2bcPVq06NvFygLVr/axebfHAAxn4fC5jxjixO3lCIZsePdwkn4GIiIiIiEjbUakjIimjSxc480ybM8+0gXoOHICamvjqWqtXW7z+usXDD3vbjxjhlTvl5d5/8/JU8oiIiIiISPpQqSMiKatTJzjtNJvTTrMBOHwY1qyJz8lTXW3x5psWv/+9t/3gwU5sCfVQyKZ/f5U8IiIiIiKSunyu6xrxW82uXfuTfQhGyciwqK+3k30Y0sbae84NDbBunZ9wOEA4bFFZabF/vy/28/79nVjBEwpFyc938flOsENDtfecpXUoZzMoZ3MoazMoZzOkQ85VVRVtuv+SkvJW21eyrndubucWPU6ljrSJdHjjkaalWs62DRs2+GPDtSKRAHv2xFucvLz4nDzl5TbDhzsqeUi9nKVllLMZlLM5lLUZlLMZ0iFnlTpNa2mpo+FXImIMy4Jx4xzGjXO47roGHAfefNMfW12rosLiL38J8pe/BAHo0cOJFTxlZTajRztYVpJPQkRERERE5CiVOiJiLL8fRo1yGDXK4ZprGnBd+Oc/fVRUeMO1wmGLZ58N8uyzXsmTk+NSWmrH5uUZN84hoHdRERERERFJEv06IiJylM8HQ4a4DBnSwFVXeSXPe+/5jhY8XtGzdGmApUu9t85OnVyKi+N38owfb5OZmeSTEBERERERY6jUERH5HD4fDBzoMnBglCuuiALwwQe+2FCtSMTi1VcDvPqq91baoYPLxInxZdQnTLDJykrmGYiIiIiISDpTqSMichL69nW59NIol17qlTy7dnklz7Fl1CsqLFau9N5ag0GX8ePt2ApbJSU22dnJPHoREREREUknKnVERL6A3FyXCy6IcsEFXsmzZw9UVsaHa1VXW1RVBfjFL8CyXMaNOzb5cpTSUpuuXZN7/CIiIiIikrpU6oiItKJu3eDcc23OPddbBnH/fli1yjq6jHqAtWv9rFlj8etfZ+DzuYweHV9GPRSy6dnTTfIZiIiIiIhIqlCpIyLShjp3hmnTbKZNs4F6Dh6Empr4nDw1NRYbNlj89rfe9sOHx4drlZfb9O6tkkdERERERBqnUkdEJIGysuDUU21OPdW7k+fIEVizxootoV5VZfGHP1j84Q/e9vn5TmwJ9VDIZsAAlTwiIiIiIuLxua5rxG8Iu3btT/YhGCUjw6K+3k72YUgbU86tr6EBXnvNHxuuVVlpsW+fL/bzU045NiePTSgUZfBgF5/vBDtsBcrZDMrZDMrZHMraDMrZDOmQc1VVRZvuv6SkvNX2lazrnZvbuUWPU6kjbSId3nikacq57dk2vPGGP7a6ViRi8ckn/tjPe/VyYnfxhEI2I0Y4+P0n2GELKGczKGczKGdzKGszKGczpEPOKnWa1tJSR8OvRETaMcuCsWMdxo51+OY3G3AceOstf6zgqaiw+Otfg/z1r0EAund3YpMul5fbjB7tYFlJPgkREREREWkTSS115s+fz+bNm5k6dSpz585t9nb79+9n3rx5OI5Dx44duffee8nIyEjgkYuIJIffDyNHOowc6fCNbzTgurBli4+KikBsXp4lS4IsWeKVPF26uJSW2rFl1MeNcwgGk3wSIiIiIiLSKlr5Jv3mW7p0KY7jsHDhQrZt28bWrVubvd0zzzzD17/+dR599FF69uzJihUrEnvwIiLthM8Hgwe7fOUrDTzwwGFWrz5ATU0d999/iDlz6unRw+Xvfw9w112ZzJjRiWHDsrnsso7ce28GkYjFkSPJPgMREREREWmppN2pU1VVxYwZMwCYMmUKNTU1DBo0qFnbXXnllbGf79mzhx49ejT5fMGg1eaTiUpcIKDxHiZQzu3TkCEwZIjLVVdFgSgffOAjHPazcqU3bGvZsgDLlnlv/5mZLpMmOUye7DB5sk1xsUNW1vH7U85mUM5mUM7mUNZmUM5mSIecLatt7yfJyGi9a5Rq1zthpc7tt9/Oli1bYt9XVVUxa9YsAHJycti+fXujjzt48CB5eXmNbrdmzRpqa2spKipq8vkbGlJ7YqlUlOqTeUnzKOf2r2dPuOAC7wtg924fkUh8GfWKCj8rV1pAkGDQpbDQobw8Snm5TXGxTY8eytkUytkMytkcytoMytkMqZ6zbTttuv/Wvj6pdL0TVuosWLDguO/vvvtuDh8+DHjFjeM0HnJWVlaj2+3du5e77rqL+++/vw2PWkQk/fTs6XL++VHOPz8KwN69UFVlxeblWbPGT3V1Jr/8Jfj9XslTWurNyVNaatOtW3KPX0REREREPEkbflVQUEBNTQ1FRUVs2rSJ/Pz8Zm9XX1/Pd7/7XW666Sb69euX4CMXEUkvXbvCOefYnHOO9y8SdXVeyXNsda01a7yv3/wmA5/PZdQoJ7a6VmmpTa9ebnJPQERERETEUD7XdZPyabyuro45c+YQCoVYvnw5ixYtYseOHSxevJh58+adcLvFixdz7733MnLkSABmz57NzJkzT/h8u3btb9PzkeNlZFgpdcuatIxyNoNtW4TDxJZRr662OHw4PknZsGHeEurHip4+fVTypCK9ns2gnM2hrM2gnM2QDjlXVVW06f5LSspbbV/Jut65uZ1b9LiklToAtbW1rFy5kuLiYnJzc7/wdieiUiex0uGNR5qmnM3w6ZyPHIG1a+Nz8lRVWRw4EC95Bg50KC+3CYWihEI2Awa4mqg+Bej1bAblbA5lbQblbIZ0yFmlTtNSstRJJJU6iZUObzzSNOVshqZyjkbhtdf8R+/kCRCJWNTWxlucfv0cysrsWNEzZIhKnvZIr2czKGdzKGszKGczpEPOKnWaplKnCSp1Eisd3nikacrZDCebs23Dxo3+oytreUO2Pv44voxlbq4TG64VCtmMHOngb9tVLqUZ9Ho2g3I2h7I2g3I2QzrkrFKnaS0tdZI2UbKIiKQny4KCAoeCAod/+7cGXBfeesv/L0uoWzzzTJBnngkC0K2bS2mpt4R6ebnNmDEOlpXkkxARERERSQEqdUREpE35fDBihMOIEQ5XX+2VPFu2+I6uruUto/7880Gef94reTp3dikttY8O2YpSWOgQDCb5JERERERE2iGVOiIiklA+Hwwe7DJ4cJQ5c6IAbNvmIxy2YkXPiy96X5BJVpbLpEnx1bXGj7fp0CG55yAiIiIi0h6o1BERkaTr39+lf/8ol18eBY6wY4cvNlwrHLZYvjzA8uXeX1mZmS4TJsTn5Jk0yaZTp+Qev4iIiIhIMqjUERGRdicvz+Xii6NcfLF3J8/u3T4qK+MlTyRiEQ57f4UFAi6FhQ7l5d4S6qWlNp1bNs+ciIiIiEhKUakjIiLtXs+eLuedF+W887ySp7YWqqq8oVqRiMXatX5qajK5/37w+13Gjo0vo15aGqV79ySfgIiIiIhIG1CpIyIiKScnB84+2+bss73lJuvqoLo6fifP6tUW69ZZPPSQt/2oUfE5ecrKbHr1cpN49CIiIiIirUOljoiIpLzsbDj9dJvTT/dKnkOHYPXqeMlTXW2xcaPFo4962w8dGp+TJxSy6ddPJY+IiIiIpB6VOiIiknY6doTJk20mT/ZKnvp6WLvWTyQSoKLCorLS4oknLJ54wtt+wACH8nKbUMibl2fgQBefL4knICIiIiLSDD7XdY3458ldu/Yn+xCMkpFhUV9vJ/swpI0pZzOkY87RKLz+up+KCm/S5UgkwN698RanTx8ndhdPebnN0KFO2pc86ZizfJZyNoeyNoNyNkM65FxVVdGm+y8pKW+1fSXreufmtmylD5U60ibS4Y1HmqaczWBCzo4DGzf6j1tGffduf+znPXs6x83JM2qUg99/gh2mIBNyFuVsEmVtBuVshnTIWaVO01pa6mj4lYiIGM/vhzFjHMaMcbj22gZcF955x7uTJxy2qKiwWLw4yOLFQQC6dnUpK4vG7uYpKHAI6G9UEREREUkwfQQVERH5FJ8Phg1zGDbM4Wtf80qerVt9RCLeMurhsMXzzwd5/nmv5MnOdikpOXYnT5SiIoeMjCSfhIiIiIikPZU6IiIiTfD5ID/fJT8/yuzZUQC2b/cdN1zr5ZcDvPxyAMikY0eXSZPic/JMmGDToUNyz0FERERE0o9KHRERkRbo189l1qwos2Z5Jc+OHd6dPMdKnhUrAqxY4f01m5HhMmFCfAn1SZNssrOTefQiIiIikg40UbK0iXSYzEuappzNoJxb5pNPIBIJxEqe11/34zjeElqBgEthoUNZmU15eZTSUpsuXZJ7vMrZDMrZHMraDMrZDOmQsyZKbpomShYREWlHuneHmTOjzJzp3cmzbx9UVVlHJ18OsG6dn5oaiwceyMDncykocGKra5WV2fToYcS/uYiIiIjIF6BSR0REJAG6dIGzzrI56ywbqOfAAaiujg/XWr3a4rXXLB56yNt+5Mj4cK1QyCYvTyWPiIiIiBxPpY6IiEgSdOoEU6faTJ3q3d57+DCsWWPFllGvrrbYtMnisce87YcMcQiF4suon3KKSh4RERER06nUERERaQc6dCBW2ADU18O6dX7CYW9enspKi//93wz+93+97QcMiM/JU1Zmk5/v4vMl8QREREREJOE0UbK0iXSYzEuappzNoJzbh2gUNmzwEw57d/NUVgbYsyfe4vTu7cRKofJym2HDnJMqeZSzGZSzOZS1GZSzGdIhZ02U3DTjJkreu3cvGzZsYNSoUXTv3j3ZhyMiItKmAgEoLHQoLHS47roGHAc2bfLH5uSpqLD4y1+C/OUvQQB69vTu5DlW9Iwe7eD3J/kkRERERKRVJfVOnfnz57N582amTp3K3Llzm71dbW0t3/rWt5g6dSpLlizhD3/4Q5PFju7USax0aJOlacrZDMo5NbgubN7so6Iivoz6Bx/EW5ycHPfoylpRysttxo51CPzLP+0oZzMoZ3MoazMoZzOkQ866U6dpKXenztKlS3Ech4ULF3LLLbewdetWBg0a1Kztdu7cyc0330xRURH79u1jw4YNnHrqqYk/CRERkXbC54OhQ12GDm3gq19twHXhvfd8RwueABUVFi+8EOCFF7y/+jt1cikpsWPLqJeWJvkEREREROSkJa3UqaqqYsaMGQBMmTKFmpqaRkudxrb70pe+BMCqVatYv349N9xwQ8KOW0REJBX4fDBwoMvAgVGuuCIKwAcf+GJDtSIRi1deCfDKK95HgY4dXSZOjA/XmjjRpmPHZJ6BiIiIiDQlYaXO7bffzpYtW2LfV1VVMWvWLABycnLYvn17o487ePAgeXl5n9nOdV2WLFlCly5dCASaPo1g0NKqIAkUCFjJPgRJAOVsBuWcPgYNgkGDXGbPjgJRdu6EcNjiH//wH/1vgH/8w/s7NSPDZeJEh/Jyh8mTbUpKHLKzk3r40gr0ejaHsjaDcjZDOuRsWW07sV9GRutdo1S73gkrdRYsWHDc93fffTeHDx8GvOLGcZxGH5eVldXodj6fjzvuuIP77ruPl19+mZkzZ57w+RsaUnsMYipK9XGf0jzK2QzKOT117QozZtjMmOF9GNqxw6ay0qKiIkAkYlFZ6ZU9//M/QSzLpbAwvox6aalNTk6yz0BaQq9ncyhrMyhnM6R6zrbd+O/7raW1r08qXe+kDb8qKCigpqaGoqIiNm3aRH5+frO3e/jhh+nVqxcXX3wx+/fvp3Pnlk0oJCIiInHdusG559qce673QWb/fqiqOra6VoC1a/2sXm3x4IMZ+HwuY8bEl1EPhWx69Eja2gsiIiIiRkra6ld1dXXMmTOHUCjE8uXLWbRoETt27GDx4sXMmzfvhNs5jsONN95IfX09w4YN44477sDXxNgqrX6VWOkwQ7s0TTmbQTmboTk5HzgANTVWbHWtmhqLI0fif/+OGBEveMrLbfLyVPK0N3o9m0NZm0E5myEdctbqV01r6epXSV3SvLa2lpUrV1JcXExubu4X3u5EVOokVjq88UjTlLMZlLMZWpLz4cOwdq038XI4bLFqlcXBg/GSJz/fobw8enTIlk3//ip5kk2vZ3MoazMoZzOkQ84qdZqWkqVOIqnUSax0eOORpilnMyhnM7RGzg0NsH69PzYnTyRisX9/vOQ55RQndhdPKBQlP9/VIgYJptezOZS1GZSzGdIhZ5U6TVOp0wSVOomVDm880jTlbAblbIa2yNm24Y03/FRUHFtGPcCePfEWJy/v+Dl5RoxwVPK0Mb2ezaGszaCczZAOOavUaVpLS52kTZQsIiIi6c2yYOxYh7FjHb71rQYcB9580x+bk6eiwuLpp4M8/XQQgB49vNW1jpU8o0c7WKm1qqiIiIhIQqnUERERkYTw+2HUKIdRoxy+8Y0GXBf++U8f4XAgNi/Ps88GefZZr+Tp0sWlrMymrCxKebnN2LEOwWCST0JERESkHVGpIyIiIknh88GQIS5DhjTwla80APDee76jQ7W8ZdSXLvW+ALKyXEpK4nfyjB9vk5mZzDMQERERSS6VOiIiItJuDBjgMmBAlCuuiAJH+PBDX2y4Vjhs8eqrAV591fv40qGDy8SJ8ZJn4kSbrKzkHr+IiIhIIqnUERERkXarTx+XSy+NcumlUQB27fIRiVjHzcuzcqX3cSYYdCkq8pZRD4VsSkpssrOTefQiIiIibUuljoiIiKSM3FyXCy6IcsEFXsmzdy9UVlqxZdRXr/azalUmv/gFWJbL2LHHllGPUlpq07VrUg9fREREpFWp1BEREZGU1bUrTJ9uM326t/RoXR1UVcXv5FmzxmLtWotf/zoDn89l9Oj4MuplZTa5uW5yT0BERETkC1CpIyIiImkjOxumTbOZNs0reQ4ehJqaeMlTU2OxYYPF737nbT98uFfulJd7X717q+QRERGR1KFSR0RERNJWVhaceqrNqad6Jc+RI7BmzbHVtSyqqiwef9zi8ce97QcN8ubkOVb09O/v4vMl8QRERERETsDnum6L/0lq69athMNhamtr6dq1K5MnT6Z///6teXytZteu/ck+BKNkZFjU19vJPgxpY8rZDMrZDKbm3NAAr73mP7qMujcvz7598RanX7/4cK3y8iiDB6d2yWNqziZS1mZQzmZIh5yrqiradP8lJeWttq9kXe/c3M4telyLS52///3v/OhHP+L000+nR48e7Nmzh+XLl3PXXXdx6qmntuhg2pJKncRKhzceaZpyNoNyNoNy9tg2vPGGPzZcKxKx+Phjf+znvXo5sfl4ysttRoxw8PtPsMN2RjmbQ1mbQTmbIR1yVqnTtJaWOi0efvXII4/w1FNP0atXr9if7d69mxtvvLFdljoiIiIiTbEsGDvWYexYh29+swHHgbfe8h+3hPpf/xrkr38NAtC9u0NpqVfwhEI2Y8Y4WFaST0JERESM0eJSJxgMHlfoAPTs2RN/Kv1zlYiIiMgJ+P0wcqTDyJEOX/96A64LW7b4CIcDR4dsWTz3XJDnnvNKns6dXUpL48O1xo1zCAaTfBIiIiKStlpc6lRXVzNhwgRc18Xn83FsFFd9fX2rHZyIiIhIe+LzweDBLoMHN3DllQ0AbNvmi93JEw4HePFF7wsyycpyKS4+VvLYFBXZdOiQ3HMQERGR9NHiUmfjxo0ARKNRAoEADQ0NBPVPUSIiImKY/v1d+vePcvnlUeAIH330ryWPxbJlAZYt8z5yZWa6TJwYn5Nn0iSbrKzkHr+IiIikrhZPlByJRLj99tu5/PLLufbaa7nmmmv44IMPuOuuu5g0aVJrH+cXpomSEysdJvOSpilnMyhnMyjntrN7t49IJL6M+oYNflzXW0IrEHApKvKWUQ+FbEpKbDq3bJ7EZlHO5lDWZlDOZkiHnDVRctMSvvrV7Nmzuf/+++nZs2fsz9577z3+8z//k0WLFrXoYNqSSp3ESoc3HmmacjaDcjaDck6c2lqorLSoqPCWUF+3zo9teyWP3+8ydmx8GfWysijdurXecytncyhrMyhnM6RDzip1mpbw1a9c16VHjx7H/Vn//v01UbKIiIjICeTkwDnn2JxzjveBsa4OVq2KD9das8Zi3TqL3/zG237UqPjqWmVlNr16tejf40RERCQNtbjUmTFjBldccQVnnnkm3bp148CBA7z66qtazlxERETkJGRnwxln2JxxhlfyHDoEq1d7Q7XCYYvqaouNGy0eecTbftiw+Jw8oZBN374qeUREREzV4uFXAJs2beKll15i586ddOnShVAoRHl569321Jo0/Cqx0uEWQWmacjaDcjaDcm6/6uthzZr4nDxVVRYHDvhiPx840IktoR4K2QwY4OLzNb4v5WwOZW0G5WyGdMhZw6+alvA5dT7t8OHDrFixgpdffpmf/vSnrbHLVqVSJ7HS4Y1HmqaczaCczaCcU0c0Cq+95o8toR6JWNTWxlucvn3jc/KUl0cZMiRe8ihncyhrMyhnM6RDzip1mpbwOXUAdu3axSuvvMJLL71ETU0NkydPZsqUKV9klyIiIiJyAoEAjB/vMH68w9y5DTgOvPGGP3YnTyRi8ec/B/nzn4MA5ObGS57TTnMZMgQ0BaKIiEh6aHGpc9lll/Hmm29y+eWXc80113DgwAF+8YtftOaxiYiIiEgT/H4oKHAoKHC49toGXBfeftsfK3gqKiyeeSbIM894JU+3bi6lpdGjd/LYFBQ4WFaST0JERERapMWlzuzZs3n55Zd5/vnn2b17N3v27OGTTz6he/fuzd7H/Pnz2bx5M1OnTmXu3Lknvd3u3bu59tprefrpp1t6GiIiIiJpxeeD4cMdhg93uPpqr+TZutVHOGwRiQSpqPDz/PNBnn/eK3k6d3YpKbGP3s0TpajIIRhM8kmIiIhIs7S41Ln00ku59NJLqa+vp6KigpdeeomLL76YvLw8/u///q/Jxy9duhTHcVi4cCG33HILW7duZdCgQSe13T333MPhw4dbegoiIiIiac/ng/x8l/z8KFdf7VJfb/P++77YEurhcICXXvK+IJOsLJeJE+Ora02YYNOhQ7LPQkRERBrT4lJn5MiR+Hw+evfujf/owOxAIMCmTZua9fiqqipmzJgBwJQpU6ipqWm01Pm87cLhMB07diQ3N7dZzxcMWp+7EoS0vkBA93GbQDmbQTmbQTmb4VjOgwfD4MEuV14ZBaJ89BGEwxYrV3rDtlasCLBihfcxMTPTZeJEh/JyhylTbIqLHTp1SuJJSLPoNW0G5WyGdMjZstp2MreMjNa7Rql2vVtc6tx8881s3LiR9957j6ysLEaOHMnYsWMpKChodPvbb7+dLVu2xL6vqqpi1qxZAOTk5LB9+/ZGH3fw4EHy8vKO266+vp4HH3yQBx54gBtuuKFZx9vQkNqzhaeiVJ+hXZpHOZtBOZtBOZuhsZy7d4fzzrM57zzv+48/9lFZ6d3JU1FhEQ57Zc/PfhYkEHApLHQIhaKUl9uUlNh06ZLgk5Bm0WvaDMrZDKmes207bbr/1r4+qXS9W1zqXH311USjUd5++23Wrl3L008/zZNPPklBQQFPPPHEZ7ZfsGDBcd/ffffdsaFTBw8exHEaDzkrK+sz2z388MPMmTOHLvoEISIiItLqevRwmTkzysyZUQBqa6GqKj5ca+1aPzU1mfzqV+D3uxQUxFfYKiuLchJTLIqIiMgX0OJS55JLLuGdd95h4MCBjB07lgsvvJAhQ4bEhmI1paCggJqaGoqKiti0aRP5+fnN3u5Pf/oTlZWVPPnkk2zcuJFbb72VH//4xy09FRERERE5gZwcOPtsm7PPtoF66uqgujq+utbq1Rbr11s89JC3/ahR9r+UPDZ5eW5Sj19ERCRd+VzXbdHfsjfffDO+z5mk5qc//WmTj6+rq2POnDmEQiGWL1/OokWL2LFjB4sXL2bevHkn3K5z586xn1911VWN3hn0abt27W/GWUlryciwUuqWNWkZ5WwG5WwG5WyGtsr50CFYs+bYUC2L6mqLQ4finxOHDHEoL49SVuZNwNyvn0qetqbXtBmUsxnSIeeqqoo23X9JSXmr7StZ1zs3t3PTGzWixaVOa6itrWXlypUUFxefcMLj5m53Iip1Eisd3nikacrZDMrZDMrZDInKub4e1q3zEw4HCIctKist6uriJc+AAd5wrWNFz6BBrha0aGV6TZtBOZshHXJWqdO0lCx1EkmlTmKlwxuPNE05m0E5m0E5myFZOUej8Prr/tgy6pFIgL174y1Onz7xOXlCIZthwxyVPF+QXtNmUM5mSIecVeo0raWlTovn1BERERERaY5AAIqKHIqKHK6/vgHHgY0b/bE5ecJhi6eeCvLUU0EAevZ0YkO1QiGbUaMcmjlto4iIiFFU6oiIiIhIQvn9MGaMw5gxDtdc04Drwjvv+P9lCXWLv/0tyN/+5pU8Xbu6lJXF5+QpKHAI6FOsiIiISh0RERERSS6fD4YNcxg2zOGrX/VKnnff9cWWUK+osHj++SDPP++VPNnZLiUlx4ZrRSkqcsjISPJJiIiIJIFKHRERERFpV3w+GDTIZdCgKLNnRwHYvt0Xm5MnHLZ4+eUAL78cADLp2NFl0qT4nDwTJth07JjccxAREUkElToiIiIi0u716+cya1aUWbO8kmfnTt9xc/KsWBFgxQrvo21Ghsv48d5QrbIym+Jim+zsZB69iIhI21CpIyIiIiIpp1cvlwsvjHLhhV7J88knUFnpDdWKRCxWrbKorPQ+6lqWS2FhfBn1khKbnJxkHr2IiEjrUKkjIiIiIimve3eYMSPKjBleybNvH6xa5d3JU1ERYN06P6tXWzzwQAY+n0tBQXwZ9bIymx493CSfgYiIyMlTqSMiIiIiaadLFzjzTJszz7SBeg4cgOrq+Jw8q1dbvPaaxcMPe9uPHGkft4x6Xp5KHhERaf9U6oiIiIhI2uvUCaZOtZk61Qbg8GFYs8aKLaNeXW2xaZPF73/vbT94sEN5eXwZ9VNOUckjIiLtj891XSP+htq1a3+yD8EoGRkW9fV2sg9D2phyNoNyNoNyNoNy/nwNDbBunZ9wOEA4bFFZabF/vy/28/7948O1QqEo+fkuPt8JdphkytoMytkM6ZBzVVVFm+6/pKS81faVrOudm9u5RY9TqSNtIh3eeKRpytkMytkMytkMyrn5bBs2bPDHVteKRALs2RNvcXr3/teSx2b4cKddlTzK2gzK2QzpkLNKnaa1tNTR8CsRERERkU+xLBg3zmHcOIfrrmvAceDNN+MlTzhs8Ze/BPnLX4IA9OjhxIZqlZXZjBnj4Pcn+SRERCTtqdQREREREWmC3w+jRjmMGuVwzTUNuC5s3uwjHA7Eip5nnw3y7LNeyZOT41Ja6g3VCoVsxo1zCOiTt4iItDL91SIiIiIicpJ8Phg61GXo0Aauusored57z3f0Lh5vXp6lSwMsXep93O7UyaWkJD5cq6jIJjMzySchIiIpT6WOiIiIiMgX5PPBwIEuAwdGueKKKAAffOCLDdUKhy1eeSXAK694H787dHCZNCm+jPrEiTYdOybzDEREJBWp1BERERERaQN9+7p86UtRvvQlr+TZtctHJBJfRn3lSot//MP7OB4MuowfH5+Tp6TEJjs7mUcvIiKpQKWOiIiIiEgC5Oa6XHBBlAsu8EqePXugsjI+XKu62qKqyvt4blku48Y5sSXUS0ttunZN4sGLiEi7pFJHRERERCQJunWDc8+1Ofdcb+nc/fth1Srr6MTLAdau9bNmjcWDD2bg87mMHu1QXu7NyVNWZtOzp5vkMxARkWRTqSMiIiIi0g507gzTptlMm2YD9Rw8CDU1XskTiXh38mzYYPHb33rbjxgRn5MnFLLp3Vslj4iIaVTqiIiIiIi0Q1lZcOqpNqee6t3Jc+QIrFkTn5Nn1SqLN9+0+MMfvO3z853YEuqnnQZ9+iTx4EVEJCF8rusaUenv2rU/2YdglIwMi/p6O9mHIW1MOZtBOZtBOZtBOaeXhgZYv94fW0a9stJi3z5f7OennOLEllAvL4+Sn+/i851gh5Jy9Jo2QzrkXFVV0ab7Lykpb7V9Jet65+Z2btHjVOpIm0iHNx5pmnI2g3I2g3I2g3JOb7YNb7zhlTyRSICKCj+ffOKP/Twvz4nNx1NebjN8uIPff4IdSrun17QZ0iFnlTpNa2mpo+FXIiIiIiJpwLJg7FiHsWMdvv1th8OHbd56yx+bk6eiwuLpp4M8/XQQgB49HEpL43PyjB7tYFlJPgkRETkpSS115s+fz+bNm5k6dSpz585t9nbRaJSzzjqL/v37A3DbbbcxYsSIRB22iIiIiEi75/fDyJEOI0c6fOMbDbgubNnio6IicHTIlsWSJUGWLPFKni5dXEpL7di8POPGOQSDST4JERE5oaSVOkuXLsVxHBYuXMgtt9zC1q1bGTRoULO2O3DgAOeddx7f+973En/gIiIiIiIpyOeDwYNdBg9u4CtfaQDgvfd8sYInHA7w9797XwBZWS7FxfE7ecaPt8nMTOYZiIjIpyWt1KmqqmLGjBkATJkyhZqamkZLnca2O3z4MK+++iqVlZUMHz6cBQsWEAic+FSCQUsTwyVQIKB7d02gnM2gnM2gnM2gnM3R3KyHDoWhQ12uuioKRPngAx/hsJ+VK71hW8uWBVi2zPucnZnpMmmSw+TJDpMn2xQXO2RlteFJSJP0mjZDOuRsWW07gVdGRutdo1S73gkrdW6//Xa2bNkS+76qqopZs2YBkJOTw/bt2xt93MGDB8nLyztuu7KyMh577DF69erF97//fZYtW8aZZ555wudvaEjtiaVSUapP5iXNo5zNoJzNoJzNoJzN0ZKse/aECy7wvgB27/YRicSXUa+o8LNypQUECQZdioq8ZdTLy22Ki206t2yeT/kC9Jo2Q6rnbNtOm+6/ta9PKl3vhJU6CxYsOO77u+++m8OHDwNeceM4jYeclZX1me1GjhxJRkYGAAUFBbz77rtteOQiIiIiImbq2dPl/POjnH9+FIC9e6Gy0huqFQ5brF7tZ9WqTH75S/D7XcaNO7aMepSyMpuuXZN6+CIiaS9pixgWFBRQU1MDwKZNm+jXr1+zt/ve977Hpk2bsG2bF198kZEjRybsuEVERERETNW1K0yfbnPnnUd44YWDvP12HX/600FuvPEIkybZbNjg59e/zuCrX81ixIhsTj89i/nzM1m8OMCuXZoLQUSktSVtTp2zzjqLOXPmsHPnTpYvX86iRYt45513WLx4MfPmzTvhdiNGjOCmm24CYNq0aZSXt96a9CIiIiIi0jzZ2TBtms20ad5QhUOHoKbGii2jXl1t8cYbFr/7nbf9sGHepMvHJl/u08dN4tGLiKQ+n+u6SXsnra2tZeXKlRQXF5Obm/uFtzuRXbv2t/QwpQUyMqyUGocoLaOczaCczaCczaCczdFesj5yBNautWIrbFVVWRw4EL9jZ9Cg+HCtUMhmwABXi5uchPaSs7StdMi5qqqiTfdfUtJ6N3ok63rn5rZsUrKkljqJpFInsdLhjUeappzNoJzNoJzNoJzN0V6zjkZh/Xp/bAn1SMRi3754i9Ovn0NZ2bE7eaIMGaKS50Taa87SutIhZ5U6TVOp0wSVOomVDm880jTlbAblbAblbAblbI5Uydq24Y03/EQi8SFbH38cn/YzN/fYnTxe0TNihIM/abOCtj+pkrN8MemQs0qdprW01EnanDoiIiIiImI2y4KxYx3GjnX4t39rwHXhrbf8seFaFRUWzzwT5JlnggB07+5QWhovecaMcbCsJJ+EiEgSqdQREREREZF2weeDESMcRoxwuPpqr+TZssUXW0I9HLZ47rkgzz3nlTydO7uUltpHh2xFKSx0CAaTfBIiIgmkUkdERERERNolnw8GD3YZPLiBK69sAGDbNh/hsHV0yFaAF1/0viCTrCyXSZPiq2uNH2/ToUNyz0FEpC2p1BERERERkZTRv79L//5RLr88Chxhxw5fbKhWJGKxfHmA5cu9X3MyM10mTLBj8/JMmmTTqVNyj19EpDWp1BERERERkZSVl+dy8cVRLr44CsDu3T4qK+PLqEci3kpbAIGAS1GRQygUpbzcpqTEpnPL5iYVEWkXVOqIiIiIiEja6NnT5bzzopx3nlfy1NZCVZU3VCsctlizxk91dSb33w9+v8vYsfFl1EtLo3TvnuQTEBE5CSp1REREREQkbeXkwNln25x9trdEcV0drFplxZZRX7PGYt06i4ce8rYfNSq+ulZZmU2vXm4Sj15E5MRU6oiIiIiIiDGys+GMM2zOOMMreQ4dgtWr48O1qqstNm60ePRRb/uhQ+Nz8pSX2/Ttq5JHRNoPlToiIiIiImKsjh1h8mSbyZO9kqe+Htau9ceWUa+stHjiCYsnnvC2HzDAObq6VpRQyGbgQBefL4knICJG87mua0TVvGvX/mQfglEyMizq6+1kH4a0MeVsBuVsBuVsBuVsDmXdeqJReP11f2x1rUgkwN698Ranb9/4nDyhkM3QoU7CSh7lbIZ0yLmqqqJN919SUt5q+0rW9c7Nbdms7Sp1pE2kwxuPNE05m0E5m0E5m0E5m0NZtx3HgY0b/bHhWuGwxe7d/tjPe/Z0jpuTZ9QoB7//BDv8ApSzGdIhZ5U6TWtpqaPhVyIiIiIiIs3k98OYMQ5jxjhce20Drgtvvx0veSoqLBYvDrJ4cRCArl1dysqisaJnzBiHgH4LE5FWorcTERERERGRFvL5YPhwh+HDHb72Na/k2brVd3R1LW9enuefD/L8817Jk53tUlp6bPLlKIWFDhkZST4JEUlZKnVERERERERaic8H+fku+flRZs+OAvD++z7CYStW9Lz0kvcFmWRluUycGF9da8IEmw4dknsOIpI6VOqIiIiIiIi0oVNOcbnssiiXXRYFjrBjh3cnz7EhWytWBFixwvvVLCPDZcKE+Jw8xcU2nTol9/hFpP1SqSMiIiIiIpJAeXkuF10U5aKLvDt5PvkEIpFArOSpqvJW2QIIBFwKC53YEuqlpTZduiTz6EWkPdHqV9Im0mGGdmmacjaDcjaDcjaDcjaHsk5t+/ZBVZU36XI4HGDdOj/RqLdOut/vUlDgrbB16qkukybV0717kg9Y2pRez4mVaqtfqdSRNqE3HjMoZzMoZzMoZzMoZ3Mo6/RSVwc1NfHhWjU1FvX1vtjPR43yhmodG7KVl2fEr3jG0Os5sVTqtFMqdRJLbzxmUM5mUM5mUM5mUM7mUNbp7fBhWL3aoqoqwIoVfqqrLQ4dipc8Q4bEh2uVl9v062fEr3xpS6/nxFKp006p1EksvfGYQTmbQTmbQTmbQTmbQ1mb4VjO9fWwbp2fcNibl6ey0qKuLl7yDBjgxJZQD4VsBg1y8flOsGNpV/R6TiyVOu2USp3E0huPGZSzGZSzGZSzGZSzOZS1GT4v52gUNmzwEw578/JEIgH27o23OH36eCXPsSFbw4Y5KnnaMb2eE0ulTjulUiex9MZjBuVsBuVsBuVsBuVsDmVthubm7DiwaZM/NidPRYXF7t3+2M979nSOm5Nn9GgHv/8EO5SE0us5sVTqnIT58+ezefNmpk6dyty5c096uzvvvJPTTjuNadOmNflcKnUSS288ZlDOZlDOZlDOZlDO5lDWZmhpzq4Lmzf7qKiIL6P+wQfxFicnx6WsLD5ca+xYh0CgNY9cToZez4mVaqVO0l6aS5cuxXEcFi5cyC233MLWrVsZNGhQs7errq5m9+7dzSp0RERERERExOPzwdChLkOHNvDVrzbguvDee76jd/F4Rc8LLwR44QXv18XsbJeSEjs2L09RkUNGRpJPQkSAJJY6VVVVzJgxA4ApU6ZQU1PTaKnT2Hb9+vXjtttuY+rUqbz44oucddZZTT5fMGhpnGgCBQJWsg9BEkA5m0E5m0E5m0E5m0NZm6E1cx42DIYNc/nqVxuABrZv91FR4WflSj8VFRYvvxzg5ZcDQCYdO7oUFzuUl9tMnuwwaZJDx46tdijyKXo9J1aqXe+ElTq33347W7ZsiX1fVVXFrFmzAMjJyWH79u2NPu7gwYPk5eUdt93TTz/N0KFDufbaa/nf//1fPvzwQ6666qoTPn9Dg25XSzTdImgG5WwG5WwG5WwG5WwOZW2Gtso5Nxcuusj7Ati500ckEp+TZ/ly7wsgI8Nl/Phjd/LYFBfbZGe3yWEZS6/nxEql652wUmfBggXHfX/33Xdz+PBhwCtuHMdp9HFZWVmf2W7jxo1cfvnl5ObmcuGFF3Lvvfc2WeqIiIiIiIhIy/Tq5XLhhVEuvDAKwJ49EInE5+RZtcqisjLAffeBZbkUFsaXUS8ttcnJSe7xi6SrpA2/KigooKamhqKiIjZt2kR+fn6zt+vQoQPbtm0D4LXXXqNv376JPHQRERERERGjdesGM2ZEmTHDK3n274eqKis2L8/atX5Wr7Z44IEMfD6XMWOc2OpaoZBNjx5GLMIs0uaStvpVXV0dc+bMIRQKsXz5chYtWsSOHTtYvHgx8+bNO+F2Pp+P+fPn8/HHHxONRvnlL38ZG6L1ebT6VWJphnYzKGczKGczKGczKGdzKGsztOecDxyAmhpvqFYkYlFTY3HkSHyS0xEjvHKnvNz7b16eSp7P055zTkeptvpVUpc0r62tZeXKlRQXF5Obm/uFtzsRlTqJpTceMyhnMyhnMyhnMyhncyhrM6RSzocPw9q1XslzbLjWwYPxkmfwYCe2hHooZNO/v0qeY1Ip53SgUqedUqmTWHrjMYNyNoNyNoNyNoNyNoeyNkMq59zQAOvX+2NLqFdWWuzfHy95+vd3KCs7didPlPx819jVjFM551SkUqedUqmTWHrjMYNyNoNyNoNyNoNyNoeyNkM65WzbsGGDP7a6ViQSYM+eeIuTlxefk6e83Gb4cMeYkiedck4FKnXaqfZa6vz4x3dy+PBh7rrrv7jjjlvIyMjk3/99Hl/60gX87W9/JzMzE4Df/vbX1NSsolu37vzwhwu4997/5u2338KyLC666FIuvPCSJJ/J8fTGYwblbAblbAblbAblbA5lbYZ0ztlx4M03/bHVtSoqLHbt8sd+3qOHE5t0ORSyGT3awbKSeMBtKJ1zbo9SrdRJ2upX7c2dd2ayeHHrXo4LLohy551HmtzunXfeOvrftxk9uoBVqyqprz/CunVrKCkp47XX1rFu3Rp+/etHePrpP/PMM08BMG/e9xk0aBBXXz2H0aMLGDp0WKsev4iIiIiISDL4/TBqlMOoUQ7f+EYDrgv//KcvNlwrHLZ49tkgzz4bBCAnx6W01KasLEp5uc24cQ4B/bYrBtD/5u1AMBiktnYvgaPvOpWVYS699HIqKysoKSmjqipCKDQZn89HSUkZW7b8k82b3wEgJ6crodBk1q1brVJHRERERETSks8HQ4a4DBnSwFVXeSXPtm2+2OpaFRUBli71vgCyslxKSuzYkK3x422ODoIQSSsqdY66884jzbqrpi0MHTqcl176e6yUef319Tz44CN897vXA/DJJx8zcuRoAPr1O4V+/U5h2bKXY4/PyenK/v3tc3iZiIiIiIhIa/P5YMAAlwEDolxxRRQ4wocf+mJ38YTDFq++GuDVV71feTt0cJk4Mb6M+oQJNllZyT0HkdagUqcdGD58BM89t5izzprOunVrqa3dy223fZ+PPvqAHTs+olOnThw6dBCAN954nbVrVx/3+H37ahkyRHfpiIiIiIiIufr0cbn00iiXXhoFYNcuH5GIddy8PCtXer8CB4Mu48fH5+QpKbHJzk7m0Yu0jL/pTaStDR8+ko0b32D48JFUVYW56qqv86tfPcysWVdQVRVh7NhCVq2qBGDt2tWxyZMB9u/fTyRSwcSJxck6fBERERERkXYnN9flggui/OQnR3jllYNs2lTHE08c5Prr6xkzxqG62uIXv8jkiiuyGDYsm+nTs7jjjkxeeMFi795kH71I8+hOnXagT5++9O8/gN69+zBmzLhYQTNxYjFPPbWIu+66h+rqKq677hvk5ORw550/YdOmjdx3338TDGZw/fX/zsCBg5J7EiIiIiIiIu1Yt24wfbrN9OneykZ1dVBVdewungBr1/pZs8bi17/OwOdzGT3aid3JEwrZ9OxpxMLRkmK0pLm0CS27ZwblbAblbAblbAblbA5lbQbl3LoOHoSamvhwrZoai8OHfbGfDx8eL3jKy216907Mr9LKObFSbUlzlTrSJvTGYwblbAblbAblbAblbA5lbQbl3LaOHIE1a+IlT1WVxcGD8ZJn0CCH8vIoZWVeyTNgQNv8aq2cE0ulTjulUiex9MZjBuVsBuVsBuVsBuVsDmVtBuWcWA0N8Npr/qPLqAeIRCz27YuXPKec4sQKnlAoyuDBLj7fCXbYTMo5sVTqtFMqdRJLbzxmUM5mUM5mUM5mUM7mUNZmUM7JZdvwxhv+2J08kYjFxx/H1yLq1ev4OXlGjHDwt2CpIuWcWCp12imVOomlNx4zKGczKGczKGczKGdzKGszKOf2xXHgrbf8xy2hvnNnvMXp3t27k+fYnDyjRztYVtP7Vc6JpVKnnVKpk1h64zGDcjaDcjaDcjaDcjaHsjaDcm7fXBe2bPERDgeoqPCKnvffj5c8Xbq4lJbaR4dsRRk3ziEY/Ox+lHNiqdRpp9prqXPo0CEWLPgh+/fvIy8vj9tuW4DP5+MXv/gfvvvdm1rteb797W/yq1893Gr7a4reeMygnM2gnM2gnM2gnM2hrM2gnFPPtm2+o3PyeMuob9kSL3myslyKi+N38owfb5OZqZwTLdVKnUArH0fK6nTnbWQufrpV93nkgos5cOfdJ9zmhReepaBgLFde+TX+67/uYtOmNxg1akyrFjoiIiIiIiKSfP37u3z5y1G+/OUocISPPvLFhmuFwxbLlgVYtsz7NT0z02XiRJspU1xKSmDSJJusrOQev7Q/KnWSrGfPXjz33GOcdtoZ3HzzD2N//q931hw5cpj587/Pvn219Ot3CoMHD6GhoYFoNMr69Ws5cOAA//M/v6Rjxyxuu+0HHD58iFNO6c/8+Xck67RERERERESkCb17u1xySZRLLokCsHu3j0jEOq7oqajwAUGCQZfCQm8Z9VDIpqTEpnPLbu6QNKJS56gDd97d5F01bWHKlNM4cuQIt976PSZMmMS///t/YH1qtqx3391Kr169uOeen3P99d/gzjt/zCOPPMT27e/zwAO/5bHHfktNTTWjRo1m1qwvM2lSCTfd9O988snHdO/eI+HnJCIiIiIiIievZ0+X88+Pcv75Xsmzdy+sXh1k+XI/kYjFmjV+qqsz+eUvwe93GTvWOTpcK0ppqU23bsk9fkk8lTpJtm3be5SVhTj99GksWPBDli59jhkzzj9um9zcXrz55kZuuOHfuOyy2bE/P/fc8wDIy+tNNNpAIBBg8eKnWbLkGfbt28eRI0cSei4iIiIiIiLSerp2hXPPdZg2rQGAujpYtSp+F8/q1Rbr1ln85jcZ+Hwuo0Y5sTl5SkttevUyYgpdo6nUSbLFi58mP38wM2acz+DBQxotYiKRCr72tWuZOvWM4/68Q4cOx33/t7/9lTPOOJNp087m29/+Zpset4iIiIiIiCRWdjaccYbNGWd4E/keOgQ1NfGSp7ra4o03LB55xNt+2DBv4uVjRU+fPip50o1KnSS7/PLZ/OhHt/Hss8+QnZ3NnXf+5DPbDB8+kv/8z+/w5z8vpFu3bnzta9c0uq/i4lL+53/+i6ef/jMAu3btpE+fvm16/CIiIiIiIpIcHTvClCk2U6Z4JU99PaxZc2x1LYuqKovHH7d4/HFv+4EDHcrLbUIhb16eAQNcfL4knoB8YVrSPAU888xfePHFFwgEAgQCAa644itMmDAp2Yd1Qlp2zwzK2QzK2QzK2QzK2RzK2gzK2QxfJOdoFF57zX90GfUAkYhFbW28xenb1/mXO3miDBmikifVljRXqSNtQn/BmEE5m0E5m0E5m0E5m0NZm0E5m6E1c7Zt2LjRHxuuFYlY7N7tj/08Nzde8oRCNiNHOvj9J9hhGlKpcxLmz5/P5s2bmTp1KnPnzm32dk8++STPPfccAPv27aOwsJAFCxac8LlU6iSW/oIxg3I2g3I2g3I2g3I2h7I2g3I2Q1vm7Lrw9tvH7uTxhmx99FG8xenWzaW0NHp0yJZNQYHDpxZrTjupVuokbU6dpUuX4jgOCxcu5JZbbmHr1q0MGjSoWdvNmTOHOXPmAHDXXXdx8cUXJ/bgRURERERERFKczwfDhzsMH+5w9dUNuC5s3eojHLaoqPCGaz3/fJDnnw8C0LmzS0lJfLhWYaFDMJjkkzBc0kqdqqoqZsyYAcCUKVOoqalptNQ50XY7duxg9+7djB07tsnnCwYt48cGJlIgkOb1rQDK2RTK2QzK2QzK2RzK2gzK2QyJznnECBgxwuXqqxuABrZt81FR4WflSouKCj8vvRTgpZcCQCZZWS4lJd7ky5MnO0yc6PCpRZpTTqq9rhJW6tx+++1s2bIl9n1VVRWzZs0CICcnh+3btzf6uIMHD5KXl9fodn/84x+ZPXt2s56/oaHp26eqqiqata/mKikpb9X9pRrdCmoG5WwG5WwG5WwG5WwOZW0G5WyGZOaclweXXOJ9AezY4YsN1QqHLV591fsCyMx0mTAhPifPpEk2nTol7dBbLJVeVwkrdT49583dd9/N4cOHAa+4cRyn0cdlZWU1up3jOFRWVjJv3rw2POq29+Mf38n5519MYWERv//97+jVK4/S0hBLlvyNq666+oSPXb26mj59+mrZchEREREREUmIvDyXiy6KctFFUQA+/tgreY4VPZGIRTjsVQ2BgEthoUN5ubeEekmJTZcuyTz69JO04VcFBQXU1NRQVFTEpk2byM/PP6ntqqurGTduHL40HFPVo0fPJgsdgDVragBU6oiIiIiIiEhS9Ojhct55Uc47zyt5amuhqso6usJWgLVr/dTUZHL//eD3uxQUxFfYKiuL0r17kk8gxSWt1DnrrLOYM2cOO3fuZPny5SxatIh33nmHxYsXH3f3TWPbAfzjH/+guLg4WYffpj788AMeffRhbr31TgD27PmE22+/hWg0Sn7+YL7//Vv5yU9+xOrV1axYsYz8/MHcccfd7NtXy91330ld3X5GjBjFd797E5988jE//OHN1NcfYcCAgRQVTWTSpBIefvhBgkdntJo//w52797FD394Mz6fj8LC8XzrWzdw2WUX0bNnT3Jze/Hee+/yrW/NJRSaksQrIyIiIiIiIu1ZTg6cfbbN2WfbQD11dVBdbcWWUV+92mL9eouHHvK2HzXK/peSxyYvL2kLdKekpC5pXltby8qVKykuLiY3N/cLb3cizVnSPBlz6vz4x3fyzjtv0alTNjt2fMTXv/5vjB8/8bhS59VXX2Lt2tXceOP3WLr0Oc46azp+v59HHnmI8eMnMmHCJAB+9av7GD58BOecM4Of/ORHTJt2NkeOHGHr1n8yc+YF/H//30/57/++lw8//ICvfW02P//5rygo8CaZ3rhxA/X1DQwbNpzrr/8Gf/jDn7jssgu5774H+elPF3D11deyfv1avvGNbzbr3LW8ohmUsxmUsxmUsxmUszmUtRmUsxnSLedDh2DNmvicPNXVFocOxUfgDB1qc+GFUW6+uT4px6clzU9CTk4OM2fObLXtUtWNN34/NqdOY8rKJlNTU82NN85lzJix+P3+RrfbuvWfXHzxlwAYPXoM7767hQkTivn9739LJFJxXCFTUlIaK3QALMvij3/8LR07duTgwYMA5OX1xu/306dPX/x+P0ns/0RERERERCQNdOwI5eU25eVecVJfD+vW+QmHA4TDFpWVFo8/HuR736vHSq2FqJIiqaWONM/rr69n+vSZFBSM5frrv8H27RfQr98pZGZmcuSIN4m067rk5w9hw4bXOOWU/mzY8Dpnn30u//jHMm655XaGDx953D47dsw67vuFC//IV75yNUOHDudrX2veimIiIiIiIiIiX0RGBhQXOxQX1/Od70A0Cq6LCp1mUqnzL9rrEuR9+/bjxz++k2g0SvfuPcjL6w3A6aefyU9/uoDHHvsdt99+F1dddTV3330HTz/9Z0aOHE1JSRnRaJRbb/0Bffv2pWfPXK677tuNPkd5+an87Gc/pWvXbnTokMmuXTsTeYoiIiIiIiIiBNRSnJSkzqmTSM2ZUycdPfLIQ7z22jr8fotAwOK66/6dwYOHtPnzptu4T2mccjaDcjaDcjaDcjaHsjaDcjaDck4szakj7co113wr2YcgIiIiIiIiIm2g8Rl3RURERERERESkXVOpIyIiIiIiIiKSglTqiIiIiIiIiIikIJU6IiIiIiIiIiIpSKWOiIiIiIiIiEgKUqkjIiIiIiIiIpKCVOqIiIiIiIiIiKQgn+u6brIPQkRERERERERETo7u1BERERERERERSUEqdUREREREREREUpBKHRERERERERGRFKRSR0REREREREQkBanUERERERERERFJQSp1RERERERERERSkEodSZi9e/eycuVKPvnkk2QfirQh5WwG5WyOL5K1/j9JHcrKDMrZDMrZDMpZjvG5rusm+yAksfbv38+8efNwHIeOHTty7733cuedd7J582amTp3K3LlzG93G7/dz1lln0b9/fwBuu+02RowY0ehzzJ8//7j91dbW8q1vfYupU6eyZMkS/vCHP9C9e/dmPRZg9+7dfOc73+HJJ59sm4uShlIt52g02uznlbhUy3nbtm3cdddd1NXVMW7cOG6++eY2uzbppr1n/en36ZN5rMSlWs5PPvkkzz33HAD79u2jsLCQBQsWtMGVSS/tOefGnjcjI0OfxVog1XI+meeVuFTLeceOHSn9WSwZ1/uY3bt3c+211/L0009/7vEl43dZ3aljoGeeeYavf/3rPProo/Ts2ZMlS5bgOA4LFy5k27ZtbN269TPbrFixgjfffJPzzjuPJ554gieeeOJzXwRLly79zP7efPNNbr75Zq6//nqmTJnChg0bmv3Y2tpafvCDH3Do0KG2vCxpJ9Vybu7zyvFSLeef/exnzJ07lyeffJKPPvqIysrKtrw8aaU9Z93Y+3RzHyvHS7Wc58yZE3vOSZMmcdlll7XJdUk37Tnnxp5Xn8VaJtVy1mexlkm1nFP9s1gyrvcx99xzD4cPH/7cY0vW77IqdQx05ZVXMnnyZAD27NnDM888w4wZMwCYMmUKNTU1n9mmR48erF27lldffZVZs2Yxf/58otFoo/uvqqr6zP5KSkooKipi1apVrF+/nvHjxzf7sZZlcd9995Gdnd2q1yHdpVrOzX1eOV6q5bx161ZGjx4NQI8ePdi/f3/rXYw0156zbux9urmPleOlWs7H7Nixg927dzN27NgvfA1M0J5zbux59VmsZVItZ30Wa5lUyznVP4sl43oDhMNhOnbsSG5u7uceW7J+lw202Z6l3VuzZg21tbX069ePvLw8AHJycti+fftntikqKsLv9/PYY4/Rq1cvvv/977Ns2TKWLVvGli1bYtuXlpZy8ODBRvfnui5LliyhS5cuBAIBbr/99mY9Vh8gvphUybmsrOwzz3vmmWe2+fVJF6mS8/Tp03nggQcoLCxkxYoV/Md//EebX5t00x6z/va3v93osX76sdJ8qZQzwB//+Edmz57dqtfABO055399XvliUiXnxp5Xn8WaL1VyTpfPYom83vX19Tz44IM88MAD3HDDDQDt6ndZfcIy1N69e7nrrru4//77eeyxx2K3kR08eBDHcT6zDcDIkSPJyMgAoKCggHfffbfRcfN33313o/vz+Xzccccd3Hfffbz88ssn9VhpmVTKubHnleZJpZznzp1LdXU1jzzyCBdffDGdOnVq5auR3tpr1p/n04+dOXNmy0/eIKmWs+M4VFZWMm/evJaftIHac86ffl5puVTKWZ/FWi6Vck6Hz2KJvt4PP/wwc+bMoUuXLrHt2tPvshp+ZaD6+nq++93vctNNN9GvXz8KCgpit5Vt2rSJfv36fWYbgO9973ts2rQJ27Z58cUXGTlyZKP7b2x/Dz/8cGxCqf3799O5c+dmP1ZaJtVybu7zyvFSLWeAUaNG8eGHH/L1r3+91a6DCdpz1o35Io81WarlDFBdXc24cePw+XwtPGvztOecG3teaZlUy1mfxVom1XKG1P4slozrHQ6HefLJJ7nqqqvYuHEjt956a7Mfmwha/cpATz75JPfee2/sf+RLL72Uxx57jFAoxPLly1m0aBGLFy8+bpvZs2czdOhQbrrpJgCmTZv2uf8iV1dXx5w5c47bn+M43HjjjdTX1zNs2DDuuOOORj/8NfbYY29SV111FU888URbXJK0lGo5f/jhh816XjlequXcuXNnfvnLXzJgwAAuvvjitrkoaao9Z33Mv75P19bWntRjxZNqOQP8/Oc/p6CggHPOOae1LkPaa885f/rYZs+eHbvLTp/FTk6q5dzc55XjpVrOM2fOTOnPYsm43v9amp3ofTBZv8uq1BHA+/C9cuVKiouLTzj5UyL219rHInHK2QzK2RztKWtpO8rZDMrZDMrZDMo5sdrT9U5GVip1RERERERERERSkObUERERERERERFJQSp1RERERERERERSkEodEREREREREZEUpFJHRERERERERCQFqdQREREREREREUlB/z/5ZSiq9oJ61AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "策略表现:\n",
      "           股票代码  总收益率  年化收益率  最大回撤  胜率  交易次数\n",
      "0     000001.SZ   0.0    0.0   0.0   0     0\n",
      "1     000002.SZ   0.0    0.0   0.0   0     0\n",
      "2     000004.SZ   0.0    0.0   0.0   0     0\n",
      "3     000006.SZ   0.0    0.0   0.0   0     0\n",
      "4     000007.SZ   0.0    0.0   0.0   0     0\n",
      "...         ...   ...    ...   ...  ..   ...\n",
      "5482  920108.SZ   0.0    0.0   0.0   0     0\n",
      "5483  920111.SZ   0.0    0.0   0.0   0     0\n",
      "5484  920116.SZ   0.0    0.0   0.0   0     0\n",
      "5485  920118.SZ   0.0    0.0   0.0   0     0\n",
      "5486  920128.SZ   0.0    0.0   0.0   0     0\n",
      "\n",
      "[5487 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# 示例使用\n",
    "if __name__ == \"__main__\":\n",
    "    # 假设df已经包含了新的股票数据格式\n",
    "    # df = pd.read_csv('new_stock_data.csv')\n",
    "    \n",
    "    # 创建策略实例\n",
    "    strategy = StockTradingStrategy(df)\n",
    "    \n",
    "    # 数据预处理\n",
    "    strategy.preprocess_data()\n",
    "    \n",
    "    # 生成交易信号\n",
    "    strategy.generate_signals()\n",
    "    \n",
    "    # 回测策略\n",
    "    strategy.backtest_strategy(initial_capital=100000, stop_loss=0.95, take_profit=1.20)\n",
    "    \n",
    "    # 选股示例\n",
    "    selected_stocks = strategy.select_and_rank_stocks('20250315')\n",
    "    print(\"选股结果:\")\n",
    "    print(selected_stocks.head())\n",
    "    \n",
    "    # 可视化单个股票结果\n",
    "    if not df.empty:\n",
    "        sample_stock = df['ts_code'].iloc[0]\n",
    "        strategy.visualize_results(sample_stock)\n",
    "    \n",
    "    # 比较多个股票\n",
    "    if len(df['ts_code'].unique()) >= 3:\n",
    "        strategy.compare_strategies(df['ts_code'].unique()[:3])\n",
    "    \n",
    "    # 获取表现指标\n",
    "    performance = strategy.get_performance_metrics()\n",
    "    print(\"\\n策略表现:\")\n",
    "    print(performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8441d545",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
